Transcript Power point file. - University of Kentucky

Cox Proportional Hazards
Regression Model
Mai Zhou
Department of Statistics
University of Kentucky
A search in the New England Journal of
Medicine, Nov. 2001 --- Nov. 2002 article
for “Cox proportional hazards” yield >300
results.
A search in Journal of American Medical
Association yields similar result.
Some quotes from abstracts of NEJM:
..The time to cancer in the two groups was compared by Kaplan–Meier
analysis and a Cox proportional-hazards model ……(5/2002)
The presence of an interaction between sex and digoxin therapy with
respect to the primary end point of death from any cause was
evaluated with the use of Mantel–Haenszel tests of heterogeneity and
a multivariable Cox proportional-hazards model, adjusted for
demographic and clinical variables. (10/2002)
With the use of Cox proportional-hazards models, the body-mass index
was evaluated …… (8/2002)
We used proportional-hazards regression models to estimate the effect
on mortality of combination therapy …… (11/2001)
Methods We estimated graft survival using proportional-hazards
techniques, adjusting for patient and donor characteristics, for a series
of 30,564 Medicare patients receiving ….
Methods In this prospective cohort study, we estimated the effects of air
pollution on mortality, while controlling for individual risk factors.
Survival analysis, including Cox proportional-hazards regression
modeling, was conducted with data…….
Exponential Random Variable
• Two ways of describing an Exponential random
variable.
1. Length of “life”…
X
CDF, pdf
2. Force of mortality, hazard, risk, intensity.
Hazard at time t
Imaging some evil force try to kill you: at time
intensity of the force is h(t ) [ must  0
The probability you die in the next small time
interval (t , t  t ]
(provided you still alive at time t ) is
h(t )t
t
the
].
h(t ) 
• If
distribution.
constant, then we get Exponential
• Easiest in the language of hazard.
• But may not be appropriate for many cases.
OK to describe an electric component
under constant working condition.
But my hazard goes up if I am going
through a high stress time, downhill skiing……
• Average US population daily hazard based on 2000
census is bath-tub shaped.
Exponential Regression Model
• Every patient’s lifetime is an exponential r.v.
• The only difference is the  constant hazard.
This patient is female, young and have
no family history of heart problems --her risk (constant) is low. (or 20% lower …)
• Constant for patient i,
depends on
,
i
his/her
covariates (gender, age, gene …..)

Exponential regression Model
(cont.)
• The constant hazard for patient i is
i  exp(  1agei   2 genderi  3trti )
The exp( ) is used to ensure the constants are
always positive.
Other positive, monotone function can also be used.
If the range of covariates is always positive then
we may get by without function.
Cox Model is Exponential model
under a variable (crazy) time clock
• “I went through two years worth of trouble
in the past two months” (faster clock)
• “life in the fast lane”
• “One year for a dog is like 7 years for a human”.
• Cox model only uses the rank order of the data to
estimate the risk ratios. Clocks do not change the rank
ordering.
• We do not have to know how fast/slow the clock
---- (semiparametric).
• But every patient uses the same crazy clock!
• Still make sense to say patient A has 20% lower
risk than average, but did not make sense to
say the risk is 0.8 without specify the clock.
• Model assumption may not always be true.
Solution----stratified Cox model
where only the people in the same
stratum share a clock; different
strata can have different clocks.
• Censoring. The lifetime observations may
be (right) censored.
• We can estimate the crazy clock’s speed.
(Kaplan-Meier estimator and its relatives.)
(but this time we use more than just the
orders of the data.)
• Easy to convert hazards to survival
probability plots.
Cox Model with time-dependent
Covariates
• Exponential r.v.  piece-wise exponential
• Evil force is only constant in an interval of
time
• In a relative short time, the hazard should
be close to constant.
• The constant may change after switching
treatment, after operation, after some
other event etc.
Cox model with time-dependent
covariates = Piece-wise exponential
regression model under crazy clock
• Piece-wise exponential has
1
hazard
in time interval
hazard
etc.

2

in time interval
(0, t1 ]
(t1 , t2 ]
• Cox model use these information as
follows:
• If a patient switches treatment (from trt
one to trt two) at time 62, then he/she will
be treated like two patients:
One dropped out of trt one at time 62 still
alive,
one entered trt two at time 62, may be die
later.